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GEML: A grammar-based evolutionary machine learning approach for design-pattern detection

JOURNAL OF SYSTEMS AND SOFTWARE 2021年 175卷 110919-110919页

作者： Barbudo, Rafael Ramirez, Aurora Servant, Francisco Romero, Jose Raul Univ Cordoba Dept Comp Sci & Numer Anal Cordoba 14071 Spain Virginia Tech Dept Comp Sci Blacksburg VA 24060 USA

Design patterns (DPs) are recognised as a good practice in software development. However, the lack of appropriate documentation often hampers traceability, and their benefits are blurred among thousands of lines of code. Automatic methods for DP detection have become relevant but are usually based on the rigid analysis of either software metrics or specific properties of the source code. We propose GEML, a novel detection approach based on evolutionary machine learning using software properties of diverse nature. Firstly, GEML makes use of an evolutionary algorithm to extract those characteristics that better describe the DP, formulated in terms of human-readable rules, whose syntax is conformant with a context-free grammar. Secondly, a rule-based classifier is built to predict whether new code contains a hidden DP implementation. GEML has been validated over five DPs taken from a public repository recurrently adopted by machine learning studies. Then, we increase this number up to 15 diverse DPs, showing its effectiveness and robustness in terms of detection capability. An initial parameter study served to tune a parameter setup whose performance guarantees the general applicability of this approach without the need to adjust complex parameters to a specific pattern. Finally, a demonstration tool is also provided. (c) 2021 Elsevier Inc. All rights reserved.

关键词： Design pattern detection Reverse engineering Machine learning Associative classification grammar-guided genetic programming

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Reverse engineering gene regulatory network based on complex-valued ordinary differential equation model

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BMC BIOINFORMATICS 2021年第SUPPL 3期22卷 448-448页

作者： Yang, Bin Bao, Wenzheng Zhang, Wei Wang, Haifeng Song, Chuandong Chen, Yuehui Jiang, Xiuying Zaozhuang Univ Sch Informat Sci & Engn Zaozhuang 277160 Peoples R China Xuzhou Univ Technol Sch Informat & Elect Engn Xuzhou 221018 Jiangsu Peoples R China Univ Jinan Sch Informat Sci & Engn Jinan 250022 Peoples R China

Background The growing researches of molecular biology reveal that complex life phenomena have the ability to demonstrating various types of interactions in the level of genomics. To establish the interactions between genes or proteins and understand the intrinsic mechanisms of biological systems have become an urgent need and study hotspot. Results In order to forecast gene expression data and identify more accurate gene regulatory network, complex-valued version of ordinary differential equation (CVODE) is proposed in this paper. In order to optimize CVODE model, a complex-valued hybrid evolutionary method based on grammar-guided genetic programming and complex-valued firefly algorithm is presented. Conclusions When tested on three real gene expression datasets from *** and Human Cell, the experiment results suggest that CVODE model could improve 20-50% prediction accuracy of gene expression data, which could also infer more true-positive regulatory relationships and less false-positive regulations than ordinary differential equation.

关键词： Gene regulatory network Complex-valued ordinary differential equation grammar-guided genetic programming Firefly algorithm

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Data-driven discovery of formulas by symbolic regression

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MRS BULLETIN 2019年第7期44卷 559-564页

作者： Sun, Sheng Ouyang, Runhai Zhang, Bochao Zhang, Tong-Yi Shanghai Univ Mat Genome Inst Shanghai Peoples R China

Discovering knowledge from data is a quantum jump from quantity to quality, which is the characteristic and the spirit of the development of science. Symbolic regression (SR) is playing a greater role in the discovery of knowledge from data, specifically in this era of exponential data growth, because SRs are able to discover mathematical formulas from data. These formulas may provide scientifically meaningful models, especially when combined with domain knowledge. This article provides an overview of SR applications in the field of materials science and engineering. Integrating domain knowledge with SR is the key and a crucial approach, which allows gaining knowledge from data quickly, accurately, and scientifically. In the data-driven paradigm, SR allows for uncovering the underlying mechanisms of materials behavior, properties, and functions, in a wide range of areas from basic academic research to industrial applications, including experiments and computations, by providing explicit interpretable models from data, in comparison with other machine-learning “black-box” models. SR will be a powerful tool for rational and automatic materials development.

关键词： symbolic regression genetic programming grammar-guided genetic programming grammatical evolution mathematic formulas

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Consistent Feature Construction with Constrained genetic programming for Experimental Physics

Consistent Feature Construction with Constrained Genetic Pro...

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IEEE Congress on Evolutionary Computation (IEEE CEC)

作者： Cherrier, Noelie Poli, Jean-Philippe Defurne, Maxime Sabatie, Franck CEA LIST F-91191 Gif Sur Yvette France Univ Paris Saclay CEA Irfu F-91191 Gif Sur Yvette France

ISBN: (纸本)9781728121536

A good feature representation is a determinant factor to achieve high performance for many machine learning algorithms in terms of classification. This is especially true for techniques that do not build complex internal representations of data (e.g. decision trees, in contrast to deep neural networks). To transform the feature space, feature construction techniques build new high-level features from the original ones. Among these techniques, genetic programming is a good candidate to provide interpretable features required for data analysis in high energy physics. Classically, original features or higher-level features based on physics first principles are used as inputs for training. However, physicists would benefit from an automatic and interpretable feature construction for the classification of particle collision events. Our main contribution consists in combining different aspects of genetic programming and applying them to feature construction for experimental physics. In particular, to be applicable to physics, dimensional consistency is enforced using grammars. Results of experiments on three physics datasets show that the constructed features can bring a significant gain to the classification accuracy. To the best of our knowledge, it is the first time a method is proposed for interpretable feature construction with units of measurement, and that experts in high-energy physics validate the overall approach as well as the

关键词： feature construction grammar-guided genetic programming high-energy physics interpretability

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Multilayered neural architectures evolution for computing sequences of orthogonal polynomials

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ANNALS OF MATHEMATICS AND ARTIFICIAL INTELLIGENCE 2018年第3-4期84卷 161-184页

作者： Barrios Rolania, Dolores Delgado Martinez, Guillermo Manrique, Daniel Univ Politecn Madrid ETS Ingn Civil Madrid Spain Univ Politecn Madrid Dept Inteligencia Artificial ETSI Informat Madrid Spain

This article presents an evolutionary algorithm to autonomously construct full-connected multilayered feedforward neural architectures. This algorithm employs grammar-guided genetic programming with a context-free grammar that has been specifically designed to satisfy three important restrictions. First, the sentences that belong to the language produced by the grammar only encode all valid neural architectures. Second, full-connected feedforward neural architectures of any size can be generated. Third, smaller-sized neural architectures are favored to avoid overfitting. The proposed evolutionary neural architectures construction system is applied to compute the terms of the two sequences that define the three-term recurrence relation associated with a sequence of orthogonal polynomials. This application imposes an important constraint: training datasets are always very small. Therefore, an adequate sized neural architecture has to be evolved to achieve satisfactory results, which are presented in terms of accuracy and size of the evolved neural architectures, and convergence speed of the evolutionary process.

关键词： Evolutionary computation grammar-guided genetic programming Artificial neural networks Orthogonal polynomials 68T05 68T20 68Q32 42C05 39A05

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Multi-level grammar genetic programming for Scheduling in Heterogeneous Networks 1

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21st European Conference on genetic programming (EuroGP)

作者： Saber, Takfarinas Fagan, David Lynch, David Kucera, Stepan Claussen, Holger O'Neill, Michael Univ Coll Dublin Sch Business Nat Comp Res & Applicat Grp Dublin Ireland Bell Labs Nokia Dublin Ireland

ISBN: (数字)9783319775531

ISBN: (纸本)9783319775531;9783319775524

Co-ordination of Inter-Cell Interference through scheduling enables telecommunication companies to better exploit their Heterogeneous Networks. However, it requires from these entities to implement an effective scheduling algorithm. The state-of-the-art for the scheduling in Heterogeneous Networks is a grammar-guided genetic programming algorithm which evolves, from a given grammar, an expression that maps to the scheduling of transmissions. We evaluate in our work the possibility of improving the results obtained by the state-of-the-art using a layered grammar approach. We show that starting with a small restricted grammar and introducing the full functionality after 10 generations outperforms the state-of-the-art, even when varying the algorithm used to generate the initial population and the maximum initial tree depth.

关键词： Telecommunication Heterogeneous Network Scheduling grammar-guided genetic programming Multi-level grammar

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On the adaptability of G3PARM to the extraction of rare association rules

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KNOWLEDGE AND INFORMATION SYSTEMS 2014年第2期38卷 391-418页

作者： Luna, J. M. Romero, J. R. Ventura, S. Univ Cordoba Dept Comp Sci & Numer Anal E-14071 Cordoba Spain

To date, association rule mining has mainly focused on the discovery of frequent patterns. Nevertheless, it is often interesting to focus on those that do not frequently occur. Existing algorithms for mining this kind of infrequent patterns are mainly based on exhaustive search methods and can be applied only over categorical domains. In a previous work, the use of grammar-guided genetic programming for the discovery of frequent association rules was introduced, showing that this proposal was competitive in terms of scalability, expressiveness, flexibility and the ability to restrict the search space. The goal of this work is to demonstrate that this proposal is also appropriate for the discovery of rare association rules. This approach allows one to obtain solutions within specified time limits and does not require large amounts of memory, as current algorithms do. It also provides mechanisms to discard noise from the rare association rule set by applying four different and specific fitness functions, which are compared and studied in depth. Finally, this approach is compared with other existing algorithms for mining rare association rules, and an analysis of the mined rules is performed. As a result, this approach mines rare rules in a homogeneous and low execution time. The experimental study shows that this proposal obtains a small and accurate set of rules close to the size specified by the data miner.

关键词： Rare association rules grammar-guided genetic programming Evolutionary computation

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Association rule mining using genetic programming to provide feedback to instructors from multiple-choice quiz data

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EXPERT SYSTEMS 2013年第2期30卷 162-172页

作者： Romero, Cristobal Zafra, Amelia Maria Luna, Jose Ventura, Sebastian Univ Cordoba Dept Comp Sci & Numer Anal E-14071 Cordoba Spain

This paper proposes the application of association rule mining to improve quizzes and courses. First, the paper shows how to preprocess quiz data and how to create several data matrices for use in the process of knowledge discovery. Next, the proposed algorithm that uses grammar-guided genetic programming is described and compared with both classical and recent soft-computing association rule mining algorithms. Then, different objective and subjective rule evaluation measures are used to select the most interesting and useful rules. Experiments have been carried out by using real data of university students enrolled on an artificial intelligence practice Moodle's course on the CLIPS programming language. Some examples of these rules are shown, together with the feedback that they provide to instructors making decisions about how to improve quizzes and courses. Finally, starting with the information provided by the rules, the CLIPS quiz and course have been updated. These innovations have been evaluated by comparing the performance achieved by students before and after applying the changes using one control group and two different experimental groups.

关键词： Educational data mining computer-based testing association rule mining grammar-guided genetic programming

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Initialization method for grammar-guided genetic programming

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KNOWLEDGE-BASED SYSTEMS 2007年第2期20卷 127-133页

作者： Garcia-Arnau, M. Manrique, D. Rios, J. Rodriguez-Paton, A. Univ Politecn Madrid Dept Artificial Intelligence Madrid Spain

This paper proposes a new tree-generation algorithm for grammar-guided genetic programming that includes a parameter to control the maximum size of the trees to be generated. An important feature of this algorithm is that the initial populations generated are adequately distributed in terms of tree size and distribution within the search space. Consequently, genetic programming systems starting from the initial populations generated by the proposed method have a higher convergence speed. Two different problems have been chosen to carry out the experiments: a laboratory test involving searching for arithmetical equalities and the real-world task of breast cancer prognosis. In both problems, comparisons have been made to another five important initialization methods. (c) 2006 Elsevier B.V. All rights reserved.

关键词： grammar-guided genetic programming initialization method tree-generation algorithm breast cancer prognosis

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Crossover and mutation operators for grammar-guided genetic programming

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SOFT COMPUTING 2007年第10期11卷 943-955页

作者： Couchet, Jorge Manrique, Daniel Rios, Juan Rodriguez-Paton, Alfonso Univ Politecn Madrid Fac Informat E-28660 Madrid Spain

This paper proposes a new grammar-guided genetic programming (GGGP) system by introducing two original genetic operators: crossover and mutation, which most influence the evolution process. The first, the so-called grammar-based crossover operator, strikes a good balance between search space exploration and exploitation capabilities and, therefore, enhances GGGP system performance. And the second is a grammar-based mutation operator, based on the crossover, which has been designed to generate individuals that match the syntactical constraints of the context-free grammar that defines the programs to be handled. The use of these operators together in the same GGGP system assures a higher convergence speed and less likelihood of getting trapped in local optima than other related approaches. These features are shown throughout the comparison of the results achieved by the proposed system with other important crossover and mutation methods in two experiments: a laboratory problem and the real-world task of breast cancer prognosis.

关键词： grammar-guided genetic programming crossover mutation breast cancer prognosis

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